Methods for automatic character detection are known from the prior art, with which methods the usable information (for example in the form of usable characters) contained in a document, for example a form, is acquired. Such methods are, for example, known under the designation ICR and OCR. ICR here stands for “intelligent character recognition” and OCR for “optical character recognition”.
As a rule a separation between usable information and noise information must be effected before the actual character recognition can be implemented. This separation is necessary for suppression of interfering image points that would otherwise cause too many errors in the character recognition. Any information on the document that is not to be associated with the usable information is thereby considered as noise information. Thus, for example, forms are typically provided with form fields that contain completion instructions such as name, address etc. The form fields (that are frequently marked by a colored frame) and the completion instructions in this case form noise characters that contain noise information to be separated from the usable information.
As a rule, the form fields and the completion instructions are printed in a color noticeable to the user, for example green. In order to acquire the usable information from such a form (which usable information is in this case given by the characters entered by the user with a pen or a typewriter), the noise information must first be suppressed in order to be able to subsequently recognize the usable information.
In particularly simple supported cases in which, for example, dark writing is to be separated from a light background, the noise information can be suppressed via a binary value formation. A first binary value is associated with those image points of the digital image of the document that are associated with the light background and a second binary value is associated with those image points that are associated with the dark writing.
However, such a method does not work reliably when the background is printed in color, as this is the case in the forms (described above) provided with color form fields and color completion instructions. It is additionally aggravating that, as a rule, the color that is used for completion of the forms is not known.
Various methods are known from the prior art with which known background colors (meaning, in the forms described above, the colors of the form fields, the colors of the completion instructions as well as the colors of the paper used for the forms) can be suppressed in a pre-processing step before the binary value formation and the subsequent character recognition. These methods (also designated as color filtering methods) are based on a color filter calculated ahead of time. However, such a color filter must be “trained” or “taught” via a suitable method. This means that, before the actual color filtering, the filter properties must be established based on a representative selection of forms so that the subsequent color filtering also works reliably when the forms to be processed exhibit differences with regard to color tone, brightness and saturation.
However, the methods described in the preceding break down when a color filtering should, for example, be effected on a stack of forms of which one comprises blue completion instructions and red usable characters while in another exactly the reverse color relationships exist (meaning that red completion constructions and blue usable characters are provided). These methods are accordingly not suitable for applications in which the colors of the usable characters and of the noise characters are not already essentially known and in which no known contrast ratio of usable characters to noise characters can be assumed. It is thus not possible to effect a separation of usable information and noise information with this method given an unknown color composition of the form to be processed.
For prior art, reference is made to the printed document DE 42 02 579. There a color filtering method is described in which a transformation of the color space RGB into the color space HSI is effected for a digital image comprised of image points. A detector compares the saturation values of the image points with a threshold. The detector forms a running length detector that detects a continuous bit series whose saturation values lie above the threshold. As soon as N pixels in succession exhibit a saturation lying above the threshold, meaning that a continuous line segment exists with a minimum saturation, it is assumed that these N pixels represent a form color. This is evidently based on the realization that such continuous line segments only occur in document regions in which no written characters are present (rather, they occur in the form background). A buffer receives the HSI signal such that, in addition to the saturation values of the aforementioned line segment, the color tone values and the brightness values of this segment are now also provided for further processing. A histogram calculation (not explained in detail) is then effected in a circuit. Finally, in an order to complete the class of the form colors, in a generator further colors are added (using variance considerations) to those colors that are determined by the detector, the buffer and the circuit cited in the preceding.
For prior art, reference is also made to the printed documents WO 01/11547 A, U.S. Pat. Nos. 5,014,328 A, 6,473,522 B1, 5,014,329 A, Schettini R ED—Vandewalle J et al.: “LOW-LEVEL SEGMENTATION OF COMPLEX COLOR IMAGES”, Signal Processing Theories and Applications, Brussels, Aug. 24-27, 1997, Proceedings of the European signal processing conference (EUSIPCO), Amsterdam, Elsevier, NL, Bd. Vol. 1 Conf. 6, 24th Aug. 1992, pages 535-538, XP000348717 (ISBN: 0-444-89587-6), DE 198 28 396 A1, DE 198 45 996 A1, DE 44 45 386 C1, EP 0 576 704 A1, US 2002/0118883 A1, “RecoStar Color Professional Plus bringt Farbe ins Spiel”, Océ Document Technologies 3/01-500-B.